The land-use mix index is a way to quantify the mixture of land-use patterns. Due to practical limitations, few studies have highlighted the validity of land-use mix indices. This paper aims to explore the potential characteristics of land-use mix indices using a three-step screening method. The data precision of indices was concluded after the first-step screening. A total of 10 virtual blocks and 217 blocks in Melbourne city center served as a case study and reflected the various land-use structures. The randomized controlled comparative trial was incorporated into the second- and third-screening to indicate the applicable condition and validity. The results illustrate that the value Herfindahl–Hirschman index related to the diversity of land-use types. The results also confirmed that Dissimilarity index-I was significantly associated with the balance status of the land-use mix. Entropy index reflects the evenness but did not correlate to the diversity or balance of the land-use mix. In addition, the study also provides a set of general recommendations for the application conditions of land-use mix indices.

Lesion detection in Computed Tomography (CT) images is a challenging task in the field of computer-aided diagnosis. An important issue is to locate the area of lesion accurately. As a branch of Convolutional Neural Networks (CNNs), 3D Context-Enhanced (3DCE) frameworks are designed to detect lesions on CT scans. The False Positives (FPs) detected in 3DCE frameworks are usually caused by inaccurate region proposals, which slow down the inference time. To solve the above problems, a new method is proposed, a dimension-decomposition region proposal network is integrated into 3DCE framework to improve the location accuracy in lesion detection.Without the restriction of "anchors" on ratios and scales, anchors are decomposed to independent "anchor strings".Anchor segments are dynamically combined in accordance with probability, and anchor strings with different lengths dynamically compose bounding boxes. Experiments show that the accurate region proposals generated by our model promote the sensitivity of FPs and spend less inference time compared with the current methods.

Previous studies have mostly examined how sustainable cities try to promote non-motorized travel by creating a walking-friendly environment. Such existing studies provide little data that identifies how the built environment affects pedestrian volume in high-density areas. This paper presents a methodology that combines person correlation analysis, stepwise regression, and principal component analysis for exploring the internal correlation and potential impact of built environment variables. To study this relationship, cross-sectional data in the Melbourne central business district were selected. Pearson’s correlation coefficient confirmed that visible green ratio and intersection density were not correlated to pedestrian volume. The results from stepwise regression showed that land-use mix degree, public transit stop density, and employment density could be associated with pedestrian volume. Moreover, two principal components were extracted by factor analysis. The result of the first component yielded an internal correlation where land-use and amenities components were positively associated with the pedestrian volume. Component 2 presents parking facilities density, which negatively relates to the pedestrian volume. Based on the results, existing street problems and policy recommendations were put forward to suggest diversifying community service within walking distance, improving the service level of the public transit system, and restricting on-street parking in Melbourne.

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Previous studies have mostly examined how sustainable cities try to promote non-motorized travel by creating a walking-friendly environment. Such existing studies provide little research that identifies how the built environment affects pedestrian volume in high-density areas. This paper presents a methodology that combines person correlation analysis, stepwise regression, and principal component analysis for exploring the internal correlation and potential impact of built environment variables. To study this relationship, cross-sectional data in the Melbourne central business district were selected. Pearson’s correlation coefficient confirmed that visible green index and intersection density were not correlated to pedestrian volume. The results from stepwise regression showed that land-use mix degree, public transit stop density, and employment density could be associated with pedestrian volume. Moreover, two principal components were extracted by factor analysis. The result of the first component yielded an internal correlation where land-use and amenities components were positively associated with the pedestrian volume. Component 2 presents parking facilities density, which negatively relates to the pedestrian volume. Based on the results, existing street problems and policy recommendations were put forward to suggest diversifying community service within walking distance, improving the service level of the public transit system, and restricting on-street parking in Melbourne.